# Best machine learning books to read this year [2022 List]

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Machine learning (ML) books are a valuable resource for IT professionals looking to develop their ML skills or pursue a career in machine learning. In turn, this expertise helps organizations automate and optimize their processes and make data-driven decisions. Machine learning books can help ML engineers learn a new skill or hone an old one.

## The most popular machine learning books

Both beginners and seasoned experts can benefit from adding machine learning books to their reading lists, though the right book depends on the learner’s goals. Some books serve as an entry point into the world of machine learning, while others build on existing knowledge.

The books on this list are roughly ranked in order of difficulty – beginners should avoid continuing the books towards the end until they have mastered the concepts introduced in the books at the top of the list.

### Machine Learning for Absolute Beginners: A Simple Introduction

*Machine learning for absolute beginners* is an excellent introduction to the field of study of machine learning. It’s a clear and concise overview of the high-level concepts that drive machine learning, so it’s ideal for beginners. The e-book format contains free downloadable resources, code exercises, and video tutorials to satisfy a variety of learning styles.

Readers will learn the basic ML libraries and other tools needed to create their first model. Additionally, this book covers data cleaning techniques, data preparation, regression analysis, clustering, and bias/variance. This book may be a little too basic for readers who want to learn more about coding, deep learning, or other advanced skills.

### The Hundred Page Machine Learning Book

As the name suggests, *The Hundred Page Machine Learning Book* provides a brief overview of machine learning and the math involved. It is suitable for beginners, but some knowledge of probability, statistics and applied math will help readers navigate the material faster.

The book covers a wide range of ML topics at a high level and focuses on those aspects of ML that have significant practical value. These include:

- Types of machine learning
- Common notation and definitions
- Fundamental algorithms
- Anatomy of a Learning Algorithm
- Neural networks and deep learning

Several reviewers said the text explains complex topics in a way that is easy for most readers to understand. It doesn’t dive too deep into any particular topic, but it does provide several practice exercises and links to other resources for further reading.

### Introduction to Machine Learning with Python

*Introduction to Machine Learning with Python* is a starting point for aspiring data scientists who want to learn more about machine learning through Python frameworks. It requires no prior knowledge of machine learning or Python, although familiarity with the NumPy and matplotlib libraries will enhance the learning experience.

In this book, readers will gain a fundamental understanding of machine learning concepts and the pros and cons of using standard ML algorithms. It also explains how all the algorithms behind various Python libraries fit together in a way that is easy to understand even for the most novice learners.

### Python machine learning for example

*Python machine learning for example* builds on existing knowledge of machine learning for engineers who want to dive deeper into Python programming. Each chapter demonstrates the practical application of common Python ML skills through real-world examples. These skills include:

This book walks through each issue with a step-by-step guide to implementing the correct Python technique. Readers should have prior knowledge of machine learning and Python, and some reviewers have recommended supplementing this guide with more theoretical reference materials for advanced understanding.

### Hands-on machine learning with Scikit-Learn, Keras and TensorFlow

*Hands-on machine learning with Scikit-Learn, Keras and TensorFlow* provides a hands-on introduction to machine learning with a focus on three Python frameworks. Readers will gain an understanding of many machine learning concepts and techniques, including linear regression, neural networks, and deep learning. Then readers can apply what they learn to hands-on exercises throughout the book.

Although this book is aimed at beginners, some reviewers have said that it requires a basic understanding of machine learning principles. With that in mind, it may be best suited to readers who want to refresh their existing knowledge through real-life examples.

### Machine Learning for Hackers: Case Studies and Algorithms to Get Started

*Machine learning for hackers* is written for experienced programmers who want to maximize the impact of their data. The text builds on existing knowledge of the R programming language to create basic machine learning algorithms and analyze datasets.

Each chapter presents a different machine learning challenge to illustrate various concepts. These include:

- Data mining
- Classification
- Ranking
- Regression
- Regularization
- Optimization

This book is best suited for intermediate level learners who are well versed in R and want to learn more about the practical applications of machine learning code. Students looking to dive into machine learning theory should opt for a more advanced book like *deep learning*, *Hands-on machine learning, *Where *Mathematics for machine learning*.

### Pattern recognition and machine learning

*Pattern recognition and machine learning* is an excellent reference for understanding statistical methods in machine learning. It provides practical exercises to introduce the reader to comprehensive pattern recognition techniques.

The text is divided into chapters that cover the following concepts:

- Probability distributions
- Linear regression and classification models
- Neural networks
- Core methods and hollow-core machines
- Graphic models
- Mixing patterns and expectation maximization
- Approximate inference
- Sampling methods
- Continuous latent variables
- Sequential data

Readers should have a thorough understanding of linear algebra and multivariate calculus, so it may be too advanced for beginners. Knowledge of basic probability theory, decision theory and information theory will also aid understanding of the material.

### Mathematics for machine learning

*Mathematics for machine learning* teaches fundamental mathematical concepts necessary for machine learning. These topics include:

- Linear algebra
- Analytical geometry
- Matrix decompositions
- Vector calculation
- Probability and statistics

Some reviewers have said that this book is more about mathematical theorems than practical application, so it is not recommended for those without previous experience in applied mathematics. However, it’s one of the few resources that bridges the gap between math and machine learning, so it’s a worthwhile investment for intermediate-level learners.

### Deep Learning (Series on Adaptive Computing and Machine Learning)

For advanced learners, *deep learning* covers the math and concepts that power deep learning, a subset of machine learning that makes human-like decisions. This book presents deep learning calculations, techniques, and research, including:

- Deep Feedback Networks
- Regularization
- Convolutional networks
- Sequence modeling
- Linear factor models
- Auto-encoders
- Learning representations

There are about 30 pages that cover practical applications of deep learning like computer vision and natural language processing, but the majority of the book deals with the theory behind deep learning. With this in mind, readers should have a working knowledge of machine learning concepts before diving into this text.

*Read next: Ultimate Machine Learning Certification Guide*